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Deep Learning Architectures : A Mathematical Approach / / by Ovidiu Calin
| Deep Learning Architectures : A Mathematical Approach / / by Ovidiu Calin |
| Autore | Calin Ovidiu |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXX, 760 p. 213 illus., 35 illus. in color.) |
| Disciplina |
006.31
006.310151 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Computer science - Mathematics
Machine learning Mathematical Applications in Computer Science Machine Learning |
| ISBN | 3-030-36721-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. . |
| Record Nr. | UNINA-9910484905703321 |
Calin Ovidiu
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Demystifying Deep Learning : An Introduction to the Mathematics of Neural Networks / / Douglas J. Santry
| Demystifying Deep Learning : An Introduction to the Mathematics of Neural Networks / / Douglas J. Santry |
| Autore | Santry Douglas J. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (259 pages) |
| Disciplina | 006.310151 |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN |
1-394-20563-5
1-394-20561-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Author -- Acronyms -- Chapter 1 Introduction -- 1.1 AI/ML - Deep Learning? -- 1.2 A Brief History -- 1.3 The Genesis of Models -- 1.3.1 Rise of the Empirical Functions -- 1.3.2 The Biological Phenomenon and the Analogue -- 1.4 Numerical Computation - Computer Numbers Are Not ℝeal -- 1.4.1 The IEEE 754 Floating Point System -- 1.4.2 Numerical Coding Tip: Think in Floating Point -- 1.5 Summary -- 1.6 Projects -- Chapter 2 Deep Learning and Neural Networks -- 2.1 Feed‐Forward and Fully‐Connected Artificial Neural Networks -- 2.2 Computing Neuron State -- 2.2.1 Activation Functions -- 2.3 The Feed‐Forward ANN Expressed with Matrices -- 2.3.1 Neural Matrices: A Convenient Notation -- 2.4 Classification -- 2.4.1 Binary Classification -- 2.4.2 One‐Hot Encoding -- 2.4.3 The Softmax Layer -- 2.5 Summary -- 2.6 Projects -- Chapter 3 Training Neural Networks -- 3.1 Preparing the Training Set: Data Preprocessing -- 3.2 Weight Initialization -- 3.3 Training Outline -- 3.4 Least Squares: A Trivial Example -- 3.5 Backpropagation of Error for Regression -- 3.5.1 The Terminal Layer (Output) -- 3.5.2 Backpropagation: The Shallower Layers -- 3.5.3 The Complete Backpropagation Algorithm -- 3.5.4 A Word on the Rectified Linear Unit (ReLU) -- 3.6 Stochastic Sine -- 3.7 Verification of a Software Implementation -- 3.8 Summary -- 3.9 Projects -- Chapter 4 Training Classifiers -- 4.1 Backpropagation for Classifiers -- 4.1.1 Likelihood -- 4.1.2 Categorical Loss Functions -- 4.2 Computing the Derivative of the Loss -- 4.2.1 Initiate Backpropagation -- 4.3 Multilabel Classification -- 4.3.1 Binary Classification -- 4.3.2 Training A Multilabel Classifier ANN -- 4.4 Summary -- 4.5 Projects -- Chapter 5 Weight Update Strategies -- 5.1 Stochastic Gradient Descent -- 5.2 Weight Updates as Iteration and Convex Optimization.
5.2.1 Newton's Method for Optimization -- 5.3 RPROP+ -- 5.4 Momentum Methods -- 5.4.1 AdaGrad and RMSProp -- 5.4.2 ADAM -- 5.5 Levenberg-Marquard Optimization for Neural Networks -- 5.6 Summary -- 5.7 Projects -- Chapter 6 Convolutional Neural Networks -- 6.1 Motivation -- 6.2 Convolutions and Features -- 6.3 Filters -- 6.4 Pooling -- 6.5 Feature Layers -- 6.6 Training a CNN -- 6.6.1 Flatten and the Gradient -- 6.6.2 Pooling and the Gradient -- 6.6.3 Filters and the Gradient -- 6.7 Applications -- 6.8 Summary -- 6.9 Projects -- Chapter 7 Fixing the Fit -- 7.1 Quality of the Solution -- 7.2 Generalization Error -- 7.2.1 Bias -- 7.2.2 Variance -- 7.2.3 The Bias‐Variance Trade‐off -- 7.2.4 The Bias‐Variance Trade‐off in Context -- 7.2.5 The Test Set -- 7.3 Classification Performance -- 7.4 Regularization -- 7.4.1 Forward Pass During Training -- 7.4.2 Forward Pass During Normal Inference -- 7.4.3 Backpropagation of Error -- 7.5 Advanced Normalization -- 7.5.1 Batch Normalization -- 7.5.2 Layer Normalization -- 7.6 Summary -- 7.7 Projects -- Chapter 8 Design Principles for a Deep Learning Training Library -- 8.1 Computer Languages -- 8.2 The Matrix: Crux of a Library Implementation -- 8.2.1 Memory Access and Modern CPU Architectures -- 8.2.2 Designing Matrix Computations -- 8.2.2.1 Convolutions as Matrices -- 8.3 The Framework -- 8.4 Summary -- 8.5 Projects -- Chapter 9 Vistas -- 9.1 The Limits of ANN Learning Capacity -- 9.2 Generative Adversarial Networks -- 9.2.1 GAN Architecture -- 9.2.2 The GAN Loss Function -- 9.3 Reinforcement Learning -- 9.3.1 The Elements of Reinforcement Learning -- 9.3.2 A Trivial RL Training Algorithm -- 9.4 Natural Language Processing Transformed -- 9.4.1 The Challenges of Natural Language -- 9.4.2 Word Embeddings -- 9.4.3 Attention -- 9.4.4 Transformer Blocks -- 9.4.5 Multi‐Head Attention -- 9.4.6 Transformer Applications. 9.5 Neural Turing Machines -- 9.6 Summary -- 9.7 Projects -- Appendix A Mathematical Review -- A.1 Linear Algebra -- A.1.1 Vectors -- A.1.2 Matrices -- A.1.3 Matrix Properties -- A.1.4 Linear Independence -- A.1.5 The QR Decomposition -- A.1.6 Least Squares -- A.1.7 Eigenvalues and Eigenvectors -- A.1.8 Hadamard Operations -- A.2 Basic Calculus -- A.2.1 The Product Rule -- A.2.2 The Chain Rule -- A.2.3 Multivariable Functions -- A.2.4 Taylor Series -- A.3 Advanced Matrices -- A.4 Probability -- Glossary -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830539803321 |
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Santry Douglas J.
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| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar
| Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar |
| Autore | Shi Bin |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXI, 133 p. 25 illus., 24 illus. in color.) |
| Disciplina |
621.382
006.310151 |
| Soggetto topico |
Telecommunication
Computational intelligence Data mining Information storage and retrieval systems Quantitative research Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Data Analysis and Big Data |
| ISBN | 3-030-17076-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. General Framework of Mathematics -- Chapter 3. Problem Formulation -- Chapter 4. Development of Novel Techniques of CoCoSSC Method -- Chapter 5. Further Discussions of the Proposed Method -- Chapter 6. Related Work on Geometry of Non-Convex Programs -- Chapter 7. Gradient Descent Converges to Minimizers -- Chapter 8. A Conservation Law Method Based on Optimization -- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations -- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series -- Chapter 11. Conclusion. |
| Record Nr. | UNINA-9910366589703321 |
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Shi Bin
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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